1 Background

A survey of the seamounts south of Tasmania on board of the Australian Marine National Facility Vessel Investigator imagery of the seafloor was collected on transects using a towed camera system. The imagery consist of Video and stereo still images. The data is structured into seamounts (Map Locations), transects (operations) and images that are geolocated in space. A selection of the stereo stills are processed to generate a quadrat of measured size overlaid on the image. The area within quadrats is annotated for (1) percent cover of substrate types with the matrix-forming coral (esp. Solenosmilia variabilis) being of particular interest, and for (2) counts of individuals of indicator taxa for ‘VME’ (Vulnerable Marine Ecosystems).

Plate 1 Example images of the habitats

Plate 1 Example images of the habitats

2 Image annotations

2.1 1. Percent cover annotations

Percent cover of substrate types is annotated using a point count approach in TransectMeasure software from SEAGIS. Random points at a density of 5 points.m-2) were thrown into the measured quadrat and annotated for 16 categories, including 3 matrix-forming coral species distinguished into live vs. dead, 4 other biogenic substrates, 5 non-biogenic substrate types and a not-scorable’ category where no category could be assigned due to image limitation or obstruction by mobile fauna. The CATAMI classification (Althaus et al. 2015) was used to define the substrate categories. Descriptive codes were used for each category for ease of data manipulation.In addition to the annotations of the random points an overall classification of the whole quadrat was applied to capture the overall impression, especially with regard to potential past fishing impact.

Codes and descriptions of the point categories used:

  • SC-ENLP: Cnidaria - Stony corals - Enalopsammia (live)
  • SC-SOL: Cnidaria - Stony corals - Solenosmilia (live)
  • SC-MAD: Cnidaria - Stony corals - Madrepora (live)
  • SU-ENLP: Cnidaria - Stony corals - Enalopsammia (dead)
  • SU-SOL: Cnidaria - Stony corals - Solenosmilia (dead)
  • SU-MAD: Cnidaria - Stony corals - Madrepora (dead)
  • SU-BCOR: Unconsolidate - Biogenic - Coral Rubble
  • SU-BBAR: Unconsolidate - Biogenic - Barnacle plates
  • SU-BOTH: Unconsolidate - Biogenic - other
  • SU-CONBIO: Consolidate - Biogenic - other (note this substrate types was not consistentlly recorded combine with xxx)
  • SU-ROK: Consolidate - Rock
  • SU-BOL: Consolidate - Boulder
  • SU-COB: Consolidate - Cobbles
  • SU-PEBGRAV: Unconsolidate - Pebble - gravel
  • SU-SAMU: Unconsolidate - Sand/mud
  • NS: Unscorable

2.2 2. VME taxa counts

Counts of indicator taxa for vulnerable marine ecosystems (VME) are made within each quadrat, allowing for calculating standardised densities per square metre. In addition, by-eye estimated of the percent cover of coral matrix (dead & alive combined) are recorded for the three types of matrix forming coral distinguished for percent cover annotations, in order to compare percent cover from point counts and estimates. Annotations are made in the CSIRO insidence of the MBARI developed Video Annotation and Registration System (VARS). The VME taxa targetted are listed below. Again, the CATAMI classification (Althaus et al. 2015) was used to classify the taxa. The comments field in VARS is used to record counts and to record additional modifyiers such as colour of distinctive taxa within some of the VME taxon groups (e.g. yellow and brown stalked crinoids were distinguished). An additional category of ‘No-VMEfauna’ was added to indicate that an image was annotated but none of the taxa of interest were observed.

VME taxa targetted:

  • Sponges
  • Stony corals: Branching: S.variabilis
  • Stony corals: Branching: Enalopsammia
  • Stony corals: Branching: Madrepora
  • stony corals (other)
  • Black & Octocorals
  • Stalked crinoids
  • Unstalked crinoid
  • Brisingid
  • True Anemones: Fourlobed
  • Hydrocorals: Branching
  • Hydrocorals (presence only recorded)

Additional taxa of interest:

  • Regular urchins
  • Dermechinus horridus (a regular urchin)
  • Irregular urchins

Percent cover of matrix forming coral (dead & alive), by-eye estimate:

  • PC_Sub_CoralReef: Coral reef species undetermined
  • PC_Cub_EnallopMatrix: Enallopsammia matrix
  • PC_Sub_SolMatrix: Solenosmilia matrix

3 Data preparation and cleaning

3.1 1. Percent cover data

The TM raw output data set is a concatenation of all TM project outputs. Each of the output files consist of the point annotations within images and the ‘user defined’ point annotation for the entire image. The raw data extract is tidied up using the separate R script Tidy_TM_Concat_data.R. This separates the raw data into overview annotations and point annotations and transformed into percent cover by substrate category for each image. Then the latter are linked to image geolocation information and written out to .cvs as column-format data (PCcover.csv) and as matrix format (PCcoverbyImage.csv), for use in further analyses and for mapping in QGIS.

Note, at this stage the overview annotations were separated out from percent cover data but are not furher cleand or considered.

3.2 2. VME taxa counts

The VME taxon counts are extracted from the VARS Oracle data base using the script VARS_2018-StillsAnnoExtracts.sql. The raw data extract is tidied up using the separate R script VME_anno_tidy.R. The non-numrical comments are separated from the counts and percent covers; these are currently not further used. The numeric data is split into percent cover data and the counts. The latter are linked to the quadrat sizes and converted to densities (ind.m-2). each of the tables is linked to image geolocation information and written out to .cvs as column-format data (VMEanno_PCcoral.csv and VMEanno_DensQ.csv) and combined as matrix format (VMEannoMatrix.csv), for use in further analyses and for mapping in QGIS.

Note, at this stage the modifyiers (colour, etc) are not incorporated into the output data.

4 Data exploration

Inital data summaries and plots based on the by image operations details and the five .csv files written out by the data cleaning process.

# read all stills data and make depth numeric

AllSTills <- read_csv("data/IN2018_V06_AllStills.csv") %>% 
  mutate(depth=as.numeric(Z)) %>% 
  select(-c(Z))

PCcoverbyImage <- read_csv("Results/PCcoverbyImage.csv")
PC_cover <- read_csv("Results/PCcover.csv")

VMEanno_DensQ <- read_csv("Results/VMEanno_DensQ.csv")
VMEanno_PCcoral <- read_csv("Results/VMEanno_PCcoral.csv")
VMEannoMatrix <- read_csv("Results/VMEannoMatrix.csv")



SelectedStlls <- AllSTills %>% 
  filter(`Selection round (1 orig sel, 2 replacement)` == 1 |
           `Selection round (1 orig sel, 2 replacement)` == 2 |
           `Selection round (1 orig sel, 2 replacement)` == 3) 

NSNR <-  SelectedStlls %>% 
  filter(RAN_SEL_STAT == "NS-NR")

In total 5985 images were potentially targeted for annotation, of these 4987 have been annotated for percent cover of substrate types, 3288 have been annotated for VME taxa and 96 were identified as unsuitable for annotation and could not be replaced. Summary of the number of images targeted for stills analyses and the number of images annotated for percent cover and for VME taxa. (NOTE need to exclude operations that were not targeted from this summary)

PC_done <- PCcoverbyImage %>% 
  group_by(SVY_OPS) %>% 
  summarise(PCdone=n())

VME_done <- VMEannoMatrix %>% 
  group_by(SVY_OPS) %>% 
  summarise(VMEdone=n())

## NOTE will need to limit the data here to target ops only
Target <- AllSTills %>% 
  group_by(SVY_OPS, MapLoc, `Target RANSMPL (1/10)`) %>% 
  filter(`Selection round (1 orig sel, 2 replacement)` == 1 |
           `Selection round (1 orig sel, 2 replacement)` == 2 |
           `Selection round (1 orig sel, 2 replacement)` == 3) %>%
  filter(RAN_SEL_STAT != "R") %>% 
  summarise(Selected = n()) %>% 
  left_join(PC_done, by=c("SVY_OPS"="SVY_OPS")) %>% 
  left_join(VME_done, by=c("SVY_OPS"="SVY_OPS"))


#tried to call this in text xxx with `r view(nTarget)` but doesn't work

kable(Target[1:6], caption="By operation processing summary")
Table 4.1: By operation processing summary
SVY_OPS MapLoc Target RANSMPL (1/10) Selected PCdone VMEdone
IN2018_V06_012 Hill U 27 50 NA NA
IN2018_V06_013 Hill U 22 39 NA NA
IN2018_V06_014 Hill U 17 33 NA NA
IN2018_V06_015 Pedra 53 95 94 NA
IN2018_V06_021 z16 18 38 31 NA
IN2018_V06_022 Pedra 21 51 50 NA
IN2018_V06_024 z16 41 41 NA NA
IN2018_V06_025 Pedra 63 63 50 33
IN2018_V06_026 Mongrel 64 64 36 NA
IN2018_V06_027 Pedra 95 97 95 NA
IN2018_V06_029 Mongrel 59 59 26 NA
IN2018_V06_031 Hill U 41 41 29 31
IN2018_V06_032 Hill U 55 54 50 33
IN2018_V06_040 Pedra 80 80 78 50
IN2018_V06_041 z16 50 50 48 30
IN2018_V06_042 Sisters 81 81 81 60
IN2018_V06_043 Sisters 83 83 81 50
IN2018_V06_044 Sisters 72 71 71 46
IN2018_V06_045 Hill K1 44 44 27 29
IN2018_V06_046 Sisters 67 67 63 43
IN2018_V06_051 Hill K1 80 81 43 42
IN2018_V06_052 Hill K1 48 48 23 24
IN2018_V06_053 Hill K1 57 57 26 26
IN2018_V06_054 Hill K1 48 48 19 19
IN2018_V06_055 Hill K1 60 60 36 36
IN2018_V06_056 Sisters 70 70 68 41
IN2018_V06_057 Sisters 67 67 61 33
IN2018_V06_059 Sisters 56 56 55 36
IN2018_V06_060 Little Sister (z56) 38 39 38 24
IN2018_V06_061 Hill U 38 38 25 23
IN2018_V06_062 z16 39 39 39 25
IN2018_V06_063 z16 43 43 46 31
IN2018_V06_064 z16 37 37 32 20
IN2018_V06_067 Sisters 63 63 63 23
IN2018_V06_068 Hill K1 45 46 15 6
IN2018_V06_075 Main Matt 66 66 65 51
IN2018_V06_080 Fang 25 25 24 NA
IN2018_V06_081 Sisters 57 57 30 29
IN2018_V06_082 Hill K1 41 41 33 31
IN2018_V06_083 Main Matt 83 83 83 67
IN2018_V06_084 Main Matt 81 81 80 65
IN2018_V06_085 Main Matt 75 75 73 56
IN2018_V06_088 Main Matt 79 79 79 60
IN2018_V06_089 Main Matt 69 69 69 57
IN2018_V06_090 slope 36 36 36 34
IN2018_V06_091 Main Matt 95 95 94 79
IN2018_V06_092 Main Matt 66 69 51 51
IN2018_V06_093 Fang 20 20 19 11
IN2018_V06_095 z99 66 67 66 47
IN2018_V06_097 Main Matt 64 64 63 47
IN2018_V06_098 z110 74 74 70 37
IN2018_V06_099 slope 76 76 66 42
IN2018_V06_100 slope 59 59 59 26
IN2018_V06_101 slope 66 66 66 NA
IN2018_V06_103 slope 60 60 33 33
IN2018_V06_104 slope 17 17 NA 9
IN2018_V06_106 slope 61 60 22 21
IN2018_V06_107 slope 15 15 9 9
IN2018_V06_108 slope 59 59 54 34
IN2018_V06_109 slope 69 69 60 35
IN2018_V06_110 Pedra 54 54 31 27
IN2018_V06_113 z16 33 31 33 28
IN2018_V06_114 z16 35 35 35 30
IN2018_V06_116 Hill U 83 83 45 49
IN2018_V06_117 Hill U 34 34 14 16
IN2018_V06_118 z34 44 44 27 16
IN2018_V06_119 z20 24 24 11 9
IN2018_V06_120 slope 51 51 20 20
IN2018_V06_121 Belinda’s Dory Hill 18 18 6 6
IN2018_V06_122 z103 64 64 26 22
IN2018_V06_123 Hill V 34 34 11 11
IN2018_V06_125 z16 36 36 34 31
IN2018_V06_127 z4 46 52 49 35
IN2018_V06_128 Punch’s Hill 22 23 22 15
IN2018_V06_129 slope 62 62 21 21
IN2018_V06_130 slope 63 63 61 35
IN2018_V06_131 Andy’s 33 33 13 7
IN2018_V06_133 slope 63 63 34 34
IN2018_V06_134 slope 69 69 28 16
IN2018_V06_136 slope 70 70 69 17
IN2018_V06_137 Patience 20 20 NA NA
IN2018_V06_138 slope 40 40 38 NA
IN2018_V06_145 z77 70 70 69 46
IN2018_V06_146 z5 72 72 71 28
IN2018_V06_147 New 1 57 57 53 35
IN2018_V06_148 z96 12 12 10 8
IN2018_V06_150 Atoll 78 77 76 32
IN2018_V06_151 z12 47 47 46 22
IN2018_V06_152 z70 31 31 30 10
IN2018_V06_154 slope 45 45 41 24
IN2018_V06_155 Punch’s Hill 30 30 30 17
IN2018_V06_156 Corvina group N 30 30 29 17
IN2018_V06_158 z53 75 75 73 49
IN2018_V06_161 z44 33 33 32 27
IN2018_V06_162 Patience 51 51 46 37
IN2018_V06_164 z91 25 25 24 13
IN2018_V06_165 z24 33 33 32 24
IN2018_V06_166 z8 70 70 67 49
IN2018_V06_167 Punch’s Hill 51 51 50 27
IN2018_V06_168 z4 37 37 36 13
IN2018_V06_172 St Helens 79 79 76 76
IN2018_V06_174 St Helens 77 77 75 74
IN2018_V06_176 St Helens 66 66 60 59
IN2018_V06_177 St Helens 68 68 65 65
IN2018_V06_179 St Helens 70 70 70 69
IN2018_V06_181 St Helens (E) 39 39 37 NA
IN2018_V06_182 St Helens 66 66 66 65
IN2018_V06_185 St Helens 71 71 71 71
IN2018_V06_186 St Helens 66 66 63 63
IN2018_V06_187 Paddys Head 56 56 NA NA
IN2018_V06_188 Paddys Head (E) 35 35 33 NA
IN2018_V06_189 Freycinet Canyon 45 45 45 NA
IN2018_V06_190 Reidle Hill 81 81 80 76
The depth distrib ution of the data set 
PCcoverbyImage %>% 
  ggplot(aes(x = depth)) +
  facet_wrap(~ MapLoc) +
  geom_histogram(bins=10)
depth distribution of the annotated imagery

Figure 4.1: depth distribution of the annotated imagery

4.1 1. Percent cover

In total there are 4987 randomly selected images have been annotated for percent cover to date. In total 106 operations have been (at least partially) annotated.

Below is a summary of the data distribution across the targeted substrate types

# check out the substrate codes that were annotated

PCsum <- PC_cover %>% 
  group_by(L2_Code) %>% 
  summarise(meanPCcover= mean(PC_cover), PresNo_Images = n())

kable(PCsum [1:3], caption="Summary of the data distribution across the targeted substrate types")
Table 4.2: Summary of the data distribution across the targeted substrate types
L2_Code meanPCcover PresNo_Images
NS 5.381557 110
SC-ENLP 9.975092 9
SC-MAD 7.459777 4
SC-SOL 13.877160 327
SU-BBAR 22.897745 164
SU-BCOR 34.058841 1216
SU-BOL 12.377628 175
SU-BOTH 28.104067 1136
SU-COB 9.133321 104
SU-CONBIO 29.371541 1710
SU-ENLP 12.801383 9
SU-MAD 4.918033 1
SU-PEBGRAV 58.894995 1189
SU-ROK 73.650442 2208
SU-SAMU 40.812051 1771
SU-SOL 60.720625 941
NA 48.639209 15

The depth distribution of the live and dead coral matrix are of particular interest in looking at the depth distribution of the substrate types.

#distribution of the substrate types
# create a vector with the sequence of the substrate types for ordering them in a meaningful way
SubstSeq <- c('SC-ENLP',
              'SU-ENLP',
              'SC-SOL',
              'SU-SOL',
              'SC-MAD',
              'SU-MAD',
              'SU-BCOR',
              'SU-BBAR',
              'SU-BOTH',
              'SU-ROK',
              'SU-BOL',
              'SU-COB',
              'SU-CONBIO',
              'SU-PEBGRAV',
              'SU-SAMU',
              'NS')
ggplot(PC_cover,
       mapping= aes(x=factor(L2_Code, level =SubstSeq),              #call the pre existing vector
                    y=depth,
                    size=PC_cover)
  )+
  geom_point(alpha=0.2)+
  scale_y_reverse() +                            # reverse y-axis because it represents ocean depth 
  theme(axis.text.x = element_text(angle = 90))+   # rotate the label on x-axis
  labs(x="substrate type", y="depth")
Depth distribution of substrate types summary graph

Figure 4.2: Depth distribution of substrate types summary graph

Coral rubble appears to have quite a high concentratio in the shallowest depths, scattering the raw data may make this easier to see:

PC_cover %>% 
  ggplot(aes(x = depth,
             y = PC_cover))+
  geom_point(alpha=0.2)+
  facet_wrap(~L2_Code)
Depth distribution of substrate types

(#fig:FigPC_DpthDist2)Depth distribution of substrate types

Distribution of the substrate types by selected locations - here the seamounts from the recovery time series are selected.

# currentl the pie graphs are not run for the knittin...
Fang <- PC_cover %>% 
  filter(MapLoc=="Fang") %>% 
  ggplot(Fang,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
    geom_bar(stat="identity", width=1)+
    ggtitle("Fang")+
    coord_polar("y", start=0)
Fang

MainMatt <- PC_cover %>% 
  filter(MapLoc=="Main Matt") %>% 
  ggplot(MainMatt,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
    geom_bar(stat="identity", width=1)+
      ggtitle("MainMatt")+
   coord_polar("y", start=0)
MainMatt

Pedra <- PC_cover %>% 
  filter(MapLoc=="Pedra") %>% 
  ggplot(Pedra,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
    geom_bar(stat="identity", width=1)+
      ggtitle("Pedra")+
    coord_polar("y", start=0)
Pedra

Sisters <- PC_cover %>% 
  filter(MapLoc=="Sisters") %>% 
  ggplot(MainMatt,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
    geom_bar(stat="identity", width=1)+
      ggtitle("Sisters")+
   coord_polar("y", start=0)
Sisters

z16 <- PC_cover %>% 
  filter(MapLoc=="z16") %>% 
  ggplot(z16,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
   geom_bar(stat="identity", width=1)+
      ggtitle("z16")+
   coord_polar("y", start=0)
z16

Hill_U <- PC_cover %>% 
  filter(MapLoc=="Hill U") %>% 
  ggplot(Hill_U,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
   geom_bar(stat="identity", width=1)+
      ggtitle("Hill_U")+
   coord_polar("y", start=0)
Hill_U

Hill_K1 <- PC_cover %>% 
  filter(MapLoc=="Hill K1") %>% 
  ggplot(Hill_U,
       mapping= aes(x="", 
                    y=PC_cover,               
                    fill=factor(L2_Code, level =SubstSeq)
       ))+
   geom_bar(stat="identity", width=1)+
      ggtitle("Hill_K1")+
   coord_polar("y", start=0)
Hill_K1

Figure 2 above shows a relatively narrow depth band where Solenosmilia (particulaly live coral) is observed, and a surprisingly high contribution of coral rubble in the shallowest images. Looking at the distribution of coral matrix formed by Solenosmilia (SC_SOL and SU_SOL) and coral rubble (SU_BCOR) in more detail, particularly by depth and location.

CoralPC <- PC_cover %>% 
  filter(L2_Code == "SC-SOL" |
         L2_Code == "SU-SOL" |
        L2_Code == "SU-BCOR"  )

CoralPC %>% 
  ggplot(aes(x = PC_cover)) +
  facet_wrap(~ L2_Code) +
  geom_histogram()
Frequency distrbution histograms of % cover recorded for coral matrix and rubble

(#fig:FigSV_histogram)Frequency distrbution histograms of % cover recorded for coral matrix and rubble

Just concentrating on the Coral rubble, why is there a high proportion of rubble in shallow sites

PC_cover %>% 
  filter(L2_Code == "SU-BCOR") %>% 
  ggplot(aes(x = depth,
             y = PC_cover))+
  geom_point(alpha=0.2)+
  facet_wrap(~MapLoc)
Distribution of coral rubble by seamount location

(#fig:FigRubble_checks)Distribution of coral rubble by seamount location

4.2 2. VME taxa

In total there are 3288 randomly selected images have been annotated for the density of VME taxa to date. In total 96 operations have been (at least partially) annotated.

# looking at the distribution of density and number of taxa over the whole data set
VME_TotDens <- VMEanno_DensQ %>% 
  filter(CONCEPT != "No-VMEfauna") %>% 
  group_by(image_key, SVY_OPS, MapLoc, depth) %>% 
  summarise(TotDens=sum(Dens),
            noTaxa=sum(NoTypes)) 

TotVME_dens <- VME_TotDens %>% 
  ggplot(aes(x=TotDens))+
  geom_histogram()

# number of Taxa 
TotVMEtax <- VME_TotDens %>% 
  ggplot(aes(x=noTaxa))+
  geom_histogram()  

# combine plots into a 2 panel figure
plot_grid(TotVMEtax, TotVME_dens)
*Frequency distribution of a) total density of VME data and b) total number of taxa

(#fig:FigPCcover_explore)*Frequency distribution of a) total density of VME data and b) total number of taxa

Instead of total density only use the totals for actual VME taxa (excluding urchins,)

# looking at the distribution of density and number of taxa over the whole data set
VMEonly_TotDens <- VMEanno_DensQ %>% 
  filter( CONCEPT == "Black & Octocorals" |
                  CONCEPT == "Brisingid"  |
                  #CONCEPT == "D.horridus",
                  CONCEPT == "Enallopsammia"  |
                  #CONCEPT == "Hydrocorals"  |
                  CONCEPT == "Hydrocorals: Branching"  |
                  #CONCEPT == "Irregular urchins"  |
                  CONCEPT == "Madrepora"  |
                  #CONCEPT == "No-VMEfauna",
                  #CONCEPT == "Regular urchins"  |
                  CONCEPT == "S.variabilis"  |
                  CONCEPT == "Sponges"  |
                  CONCEPT == "Stalked crinoids"  |
                  CONCEPT == "Stony corals",
                  #CONCEPT == "True anemones: Fourlobed"  |
                  #CONCEPT == "Unstalked crinoids"
          )%>% 
  group_by(image_key, SVY_OPS, MapLoc, depth) %>% 
  summarise(VMEtaxaDens=sum(Dens))

VMEonly_TotDens %>% 
  ggplot(aes(x = VMEtaxaDens))+
  geom_histogram()
**Frequency distribution of total density of VME taxa only

(#fig:FigDensity_explore)**Frequency distribution of total density of VME taxa only

4.3 Combined substrate and VME taxa data

Combining the totals of the VME taxa annotations with the percent cover annotations allows us to examine correlations between Total VME fauna density and substrate types.

Checking how well the estimated percent cover of coral matrix matches with measured percent cover of coral matrix (dead & alive)

VME_AnnoAll %>% 
  ggplot(mapping = aes(x= (`SC-SOL`+`SU-SOL`), 
                     y= PC_SolMatrix))+
   geom_point()
Comparison of estimated and measured percent cover of matrix

Figure 4.3: Comparison of estimated and measured percent cover of matrix 

estVmeas_lm <- lm(PC_SolMatrix ~ (`SC-SOL`+`SU-SOL`), data = VME_AnnoAll)

estVmeas_lm
## 
## Call:
## lm(formula = PC_SolMatrix ~ (`SC-SOL` + `SU-SOL`), data = VME_AnnoAll)
## 
## Coefficients:
## (Intercept)     `SC-SOL`     `SU-SOL`  
##     -0.1761       1.9940       0.6574

Looking into the overview scores of potential gear impacts for still images

# data exploration for overview scoring
OvCat<- PCcoverbyImage %>% 
  group_by(OV_CAT) %>% 
  summarise(cntRec = n())

 kable(OvCat, caption="overview categories scored")
(#tab:Tb_overviw)overview categories scored
OV_CAT cntRec
High - reef 330
High - shaved 158
HighAb - no other 39
HighAb - with clumps - dead 28
HighAb - with clumps - live 2
HighAb - with other 303
HighAb - with patch - dead 15
HighAb - with patch - live 13
Isolated fragments/ clumps 197
Low - sediment filled 155
Low - shaved 156
Low - with clumps - dead 12
Low - with clumps - live 2
Low - with patch - dead 28
Low - with patch - live 64
LowAb - no clumps 97
LowAb - with clumps 29
MedAb 481
MedAb - with clumps - dead 270
MedAb - with clumps - live 9
MedAb - with patch - dead 64
MedAb - with patch - live 37
No rubble or matrix 2430
Shaved 41
NA 27 
PCcoverbyImage %>% 
  ggplot(aes(x = depth,
             y = `SU-BCOR`,
             colour = OV_CAT))+
  geom_point(alpha=0.2)+
  facet_wrap(~MapLoc)
Impact overview evaluation

Figure 4.4: Impact overview evaluation 

PCcoverbyImage %>% 
  ggplot(aes(x = depth,
             y = (`SU-SOL`+ `SC-SOL`),
             colour = OV_CAT))+
  geom_point(alpha=0.2)+
  facet_wrap(~MapLoc)
Impact overview evaluation

Figure 4.5: Impact overview evaluation 

PC_cover %>% 
   group_by(L2_Code) %>% 
   ggplot(aes(x = `OV_CAT`,
              y = mean(PC_cover),
              colour = L2_Code)) +
     geom_col() +
   theme(axis.text.x.bottom = element_text(angle = 90))
Impact overview evaluation

Figure 4.6: Impact overview evaluation

Looking at the distribution of the overview categories by seamount will help to identify locations for further scrutiny:

## PC_cover %>%
#  filter(
#        OV_CAT != `High - reef` &
#          OV_CAT != `Low - sediment filled`) %>% 
#group_by(MapLoc, OV_CAT) %>% 
#   ggplot(aes(x = MapLoc,
#              y = mean(PC_cover),
#              colour = OV_CAT)) +
 #    geom_col() +
#   theme(axis.text.x.bottom = element_text(angle = 90))